5abf128d64
This matches loops with a affine.min upper bound, limiting the trip count to a constant, and rewrites them into two loops, one with constant upper bound and one with variable upper bound. The assumption is that the constant upper bound loop will be unrolled and vectorized, which is preferable if this is the hot path. Differential Revision: https://reviews.llvm.org/D75240
77 lines
2.9 KiB
C++
77 lines
2.9 KiB
C++
//===- ParallelLoopSpecialization.cpp - loop.parallel specializeation -----===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// Specializes parallel loops for easier unrolling and vectorization.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/AffineOps/AffineOps.h"
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#include "mlir/Dialect/LoopOps/LoopOps.h"
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#include "mlir/Dialect/LoopOps/Passes.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/IR/AffineExpr.h"
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#include "mlir/IR/BlockAndValueMapping.h"
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#include "mlir/Pass/Pass.h"
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using namespace mlir;
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using loop::ParallelOp;
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/// Rewrite a loop with bounds defined by an affine.min with a constant into 2
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/// loops after checking if the bounds are equal to that constant. This is
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/// beneficial if the loop will almost always have the constant bound and that
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/// version can be fully unrolled and vectorized.
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static void specializeLoopForUnrolling(ParallelOp op) {
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SmallVector<int64_t, 2> constantIndices;
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constantIndices.reserve(op.upperBound().size());
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for (auto bound : op.upperBound()) {
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auto minOp = dyn_cast_or_null<AffineMinOp>(bound.getDefiningOp());
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if (!minOp)
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return;
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int64_t minConstant = std::numeric_limits<int64_t>::max();
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for (auto expr : minOp.map().getResults()) {
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if (auto constantIndex = expr.dyn_cast<AffineConstantExpr>())
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minConstant = std::min(minConstant, constantIndex.getValue());
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}
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if (minConstant == std::numeric_limits<int64_t>::max())
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return;
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constantIndices.push_back(minConstant);
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}
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OpBuilder b(op);
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BlockAndValueMapping map;
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Value cond;
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for (auto bound : llvm::zip(op.upperBound(), constantIndices)) {
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Value constant = b.create<ConstantIndexOp>(op.getLoc(), std::get<1>(bound));
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Value cmp = b.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq,
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std::get<0>(bound), constant);
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cond = cond ? b.create<AndOp>(op.getLoc(), cond, cmp) : cmp;
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map.map(std::get<0>(bound), constant);
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}
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auto ifOp = b.create<loop::IfOp>(op.getLoc(), cond, /*withElseRegion=*/true);
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ifOp.getThenBodyBuilder().clone(*op.getOperation(), map);
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ifOp.getElseBodyBuilder().clone(*op.getOperation());
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op.erase();
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}
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namespace {
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struct ParallelLoopSpecialization
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: public FunctionPass<ParallelLoopSpecialization> {
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void runOnFunction() override {
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getFunction().walk([](ParallelOp op) { specializeLoopForUnrolling(op); });
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}
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};
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} // namespace
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std::unique_ptr<Pass> mlir::createParallelLoopSpecializationPass() {
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return std::make_unique<ParallelLoopSpecialization>();
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}
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static PassRegistration<ParallelLoopSpecialization>
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pass("parallel-loop-specialization",
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"Specialize parallel loops for vectorization.");
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